README.md

In SharonLutz/powerGcE: runs the powerGcE function

powerGcE

Empirical power analysis for a gene by environment interaction with a binary outcome and a normally distributed environmental exposure.

Installation

install.packages("devtools") # devtools must be installed first

devtools::install_github("SharonLutz/powerGcE")

Input

For n cases and controls (input: nCases, nControls), the SNP is generated from a binomial distribution with a specified MAF (input: MAF) and the environmental exposure E is generated from a normal distributions with user specified mean and variance (input: meanE, varE). The binary outcome Y is generated from a binomial distribution such that:

logit[P(Y=1)] = β0 + βSNPSNP + βEE + βI E*SNP

where βI is inputted as a vector of values (input: betaI). Then, the empirical power is calculated based on the proportion of simulations where the p-value for the interaction term in a logistic regression is less than the user specified alpha level.

See the manpage for more detail regarding the input of the powerGcE function.

library(powerGcE)
?powerGcE # For details on this function

Example

For 407 cases and 376 controls, consider a SNP with a MAF of 0.49 and a normally distributed environmental exposure with a mean of 0 and a variance of 0.99. The binary outcome Y is generated such that

logit[P(Y=1)] = -0.32 + 0.17SNP + 0.97E + βI E*SNP

where βI varies from -1 to -0.75 by 0.05. The code to run this example is given below.

library(powerGcE)
powerGcE(nCase=407,nControl=376,MAF=0.49,meanE=0,varE=0.99,beta0=-0.32,betaSNP=0.17,betaE=0.97,
betaI=seq(-1,-0.75,by=0.05),nSim=1000,alpha=0.00000005,plot.output=T,plot.name="powerGcE.pdf",seed=1)

Output

For this example, we get the following matrix and corresponding plot which outputs the empirical power to detect the SNP by environment interaction on the binary outcome. We can see from the plot below that we have adequate power in this scenario.

     BetaI power
[1,] -1.00 0.996
[2,] -0.95 0.996
[3,] -0.90 0.981
[4,] -0.85 0.962
[5,] -0.80 0.908
[6,] -0.75 0.833

Notes

Please note that this is an empirical power analysis. The MAF, mean, variances, and βs need to be chosen such that there is enough variability for the SNP, the environmental variable E, and the binary outcome Y.

SharonLutz/powerGcE documentation built on Nov. 25, 2019, 12:33 a.m.